Lead Data/AI Engineering

The Lead Data/AI Engineering role will help shape how data and AI are leveraged across the enterprise, enabling smarter decisions, faster innovation, and scalable AI adoption.

• →Databricks Application Development: Design, build, deploy, and support production‑grade Databricks applications that connect enterprise data, AI capabilities, and business workflows, using HTML for user interfaces and SQL for data access and transformation.
• →AI Engineering: Design, build, and operationalize AI and machine learning solutions that address business needs, ensuring reliability, performance, and responsible use in production environments.

• →Data Engineering & Pipelines: Design, build, and support scalable ETL and data pipelines that ingest, clean, transform, and serve data for analytics, machine learning, and AI workloads.

• →Cloud & Platform Enablement: Develop and integrate solutions across Snowflake, Azure, ADLS, Databricks, APIs, and other enterprise data platforms to deliver secure, scalable, and performant data and AI applications.
• →AI Operations & Quality: Implement MLOps practices, including monitoring, evaluation, and lifecycle management to ensure AI systems remain accurate, scalable, and reliable over time.

• →Business Partnership: Collaborate with business partners and technical stakeholders to understand use cases, translate requirements into technical designs, and iteratively deliver high‑value data and AI solutions.
• →Governance & Security: Ensure data integrity, security, and compliance across the data and AI lifecycle, supporting responsible AI practices and enterprise risk management standards.

• Databricks Application Development Experience: Hands‑on experience building, deploying, and supporting Databricks applications is required, including integrating data sources, developing user interfaces, and delivering applications for business users.
• Strong Engineering Foundation: Proficiency in Python, SQL, and HTML, with experience building production‑grade data applications and AI solutions. Strong SQL skills for querying, transforming, and integrating data are essential.

• Data Pipeline Expertise: Hands‑on experience designing and supporting ETL processes and large‑scale data pipelines for analytics and AI workloads.

• Snowflake & Azure Experience: Hands‑on experience developing and integrating solutions using Snowflake and Azure is required, including data storage, processing, and secure access, along with experience working with ADLS.
• Applied AI Experience: Practical experience supporting AI and machine learning solutions, including data preparation, integration, deployment, and operational considerations.

• Problem‑Solving Mindset: Strong analytical skills with the ability to troubleshoot complex data and AI pipeline issues end‑to‑end.

• Lead‑Level Experience: Demonstrated experience as a Lead or advanced data engineer, AI engineer, or similar role delivering complex data and AI solutions.

• Enterprise Application Delivery: Experience delivering Databricks applications and AI‑enabled solutions in enterprise environments.

• Collaboration & Communication: Ability to work cross‑functionally and clearly communicate technical concepts to both technical and non‑technical stakeholders.
• Continuous Improvement: A passion for learning and staying current with emerging data, AI, and cloud technologies—and applying them thoughtfully in production environments.

Changing the speed of business comes with many rewards—starting with your impact.

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